The ABCD study of neurodevelopment: Identifying neurocircuit targets for prevention and treatment of adolescent substance abuse

Optimization of self- or parent-reported psychiatric phenotypes in longitudinal studies

BACKGROUND

The Adolescent Brain Cognitive Development (ABCD) study is a longitudinal study of US adolescents with a wide breadth of psychiatric, neuroimaging and genetic data that can be leveraged to better understand psychiatric diseases. The reliability and validity of the psychiatric data collected have not yet been examined. This study aims to explore and optimize the reliability/validity of psychiatric diagnostic constructs in the ABCD study.

METHODS

Parent-and-child-reported psychiatric data for 11,876 children (aged 9.5 ± 0.5 at first assessment) were examined over 4 years to derive specific constructs for psychiatric diagnoses using longitudinal information. Rates of psychiatric disorders were calculated and compared to those reported in the epidemiological literature.

RESULTS

The rates of self-reported psychiatric disorders at any single time point (broad diagnostic construct) were higher than indicated by epidemiological studies. Narrow diagnostic constructs, which required the endorsement of psychiatric disorders at a majority of longitudinal assessments, demonstrated a better rate approximation of literature-reported prevalences for most disorders (e.g. the prevalence of broad obsessive-compulsive disorder (OCD) was 13.3% compared to narrow OCD at 2.6% and a literature-reported prevalence of 2.3%). Analysis of comorbidity, using OCD as a representative example, also showed a better approximation of literature-reported comorbidity rates using the narrow construct, with some exceptions.

CONCLUSIONS

Self- or parent-report-based assessments tend to overestimate prevalences of psychiatric disorders in the ABCD Study, particularly when longitudinal data are summed to create lifetime prevalences. Such assessments should be accompanied by more in-depth assessments or clinician-administered structured interviews if using data where accurate disorder classifications are paramount.

Developmental differences in striatal recruitment by reward prospects as a function of attentional demand

Adolescent risk-taking has been attributed to earlier-developing motivational neurocircuitry that is poorly controlled by immature executive-control neurocircuitry. Functional magnetic resonance imaging findings of increased ventral striatum (VS) recruitment by reward prospects in adolescents compared to adults support this theory. Other studies found blunted VS recruitment by reward-predictive cues in adolescents compared to adults. Task features may explain this discrepancy but have never been systematically explored. Adolescents and adults performed a novel reward task that holds constant the expected value of all rewards but varies whether rewards are dependent on vigilance-intensive responding versus making a lucky choice during a relaxed response window. We examined group by sub-task contrast differences in activation of VS and more motoric regions of striatum in response to anticipatory cues. Reward anticipation in both task conditions activated portions of striatum in both groups. In voxel-wise comparison, adults showed greater anticipatory recruitment of VS in trials involving choice during a relaxed time window, not in the more vigilance-demanding trials as hypothesized. In accord with our hypotheses, however, adults showed greater activation in dorsal striatum and putamen volumes of interest during reward anticipation under vigilance-demanding conditions. Following trial outcome notifications, adolescents showed greater activation of the VS during reward notification but lower activation during loss notification. These data extend findings of cross-sectional age-group differences in incentive-anticipatory recruitment of striatum, by demonstrating in adults relatively greater recruitment of motor effector regions of striatum by attentional and motor demands.

Characteristics of Adolescents with and without a Family History of Substance Use Disorder from a Minority Cohort

Family history (FH+) of substance use disorder (SUD) is an established risk factor for offspring SUD. The extent to which offspring psychological traits or the family environment, each of which may be relevant to familial transmission of SUD risk, vary by FH+ in socioeconomically disadvantaged populations is less clear. We compared the family/social environmental and psychological characteristics of 73 FH+ and 69 FH- youth ages 12-16, from a study of parental criminal justice system involvement in a primarily low-income, minority urban population. A latent profile analysis (LPA) empirically identified groups of subjects with similar psychological characteristics, which were then compared by FH+. FH+ youths were found to have greater mean household size, greater parental psychological aggression, and a higher mean number of adverse childhood experiences, even without considering parental SUD. FH+ individuals had lower report card grades according to parental report and were more likely to have a history of externalizing disorders than FH- individuals. However, FH+ was not significantly associated with many psychological characteristics or with the class membership from the LPA. In conclusion, among a population of low-income, minority urban youth, FH+ was associated with differences in the family environment and only subtle differences in individual psychological characteristics.

Emotion Recognition and Self-Reported Emotion Processing in Alcohol and Cannabis Co-Using Young Adults

Alcohol and cannabis use are each associated with impairments in emotion recognition accuracy, which may promote interpersonal problems. It is unclear if emotion recognition or self-reported emotion processing differs between young adult alcohol and cannabis co-users (ACCs) and healthy controls (HCs). This study examined whether ACCs and HCs differed in their emotion recognition across two different behavioral tasks with static or dynamic faces and determined if there were differences in self-reported socio-emotional processing and alexithymia. 22 ACCs (mean age = 21.27 ± 1.75) and 25 HCs (mean age = 21.48 ± 2.68), matched on age, sex, and IQ, completed the Metrisquare Emotion Recognition Task and CANTAB Emotion Recognition Task. The ACCs and HCs were compared on task accuracy and self-reported measures, including the Social Emotional Questionnaire (SEQ) and the Perth Alexithymia Questionnaire (PAQ). No significant main effects of the Group variable or the Emotion-Group interaction variable were present for either task. The ACCs had lower SEQ (p = 0.014) and higher PAQ (p = 0.024) scores relative to the HCs, indicating greater difficulties in socio-emotional processing and identifying one's own emotions, respectively. Understanding the behavioral correlates of the self-reported difficulties in emotion processing reported by ACCs is needed to develop interventions to reduce these symptoms and promote healthy socio-emotional functioning in this population.

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

Household Income and Subsequent Youth Tobacco Initiation: Minorities' Diminished Returns

Introduction

Household income, a prominent socioeconomic status (SES) indicator, is known to mitigate youth engagement in various health risk behaviors, including tobacco use. Nevertheless, the Minorities' Diminished Returns theory suggests that this protective effect may be less pronounced for racial and ethnic minorities compared to majority groups. This study aimed to investigate the protective role of high household income against tobacco use among youth and explore potential variations across different racial and ethnic groups.

Methods

Conducted as a longitudinal analysis, this study utilized data from the initial three years of the Adolescent Brain Cognitive Development (ABCD) Study spanning 2016-2022. The cohort consisted of 11,875 American youth aged 9-10 years, tracked over a three-year period. The dependent variable was tobacco initiation, irrespective of the product, while household income served as the independent variable. Covariates included youth age, gender, family education, structure, and employment, with race/ethnicity acting as the moderating variable.

Results

Out of the 8,754 American youth who were non-smokers at baseline, 3.1% (n = 269) initiated tobacco use during the 30-month follow-up, while 96.9% (n = 8,485) remained non-smokers. A family income exceeding $100,000 per year was associated with a lower hazard ratio for tobacco initiation (transitioning to ever-use) over the follow-up period (HR = 0.620, p = 0.022). However, household income of $50-100k exhibited significant interactions with race/ethnicity on tobacco initiation, indicating weaker protective effects for Black (HR for interaction = 7.860, p < 0.001) and Latino (HR for interaction = 3.461, p = 0.001) youth compared to non-Latino White youth.

Conclusions

Within the United States, the racialization and minoritization of youth diminish the protective effects of economic resources, such as high household income, against the transition to tobacco use. Non-Latino White youth, the most socially privileged group, experience greater protection from their elevated household income regarding tobacco initiation compared to Black and Latino youth, who face minoritization and racialization. Policymakers should address not only the SES gap but also the mechanisms contributing to the heightened risk of tobacco use among racialized and minoritized youth from affluent backgrounds.

Sex Differences in the Relationship Between Nucleus Accumbens Volume and Youth Tobacco or Marijuana Use Following Stressful Life Events

Background

Exposure to stressful life events (SLEs) can upset balance and affect the healthy brain development of children and youths. These events may influence substance use by altering brain reward systems, especially the nucleus accumbens (NAc), which plays a key role in motivated behaviors and reward processing. The interaction between sensitization to SLEs, depression, and substance use might vary between male and female youths, potentially due to differences in how each sex responds to SLEs.

Aims

This study aims to examine the effect of sex on the relationship between SLEs, Nucleus Accumbens activity, and substance use in a nationwide sample of young individuals.

Methods

We utilized data from the Adolescent Brain Cognitive Development study (ABCD), a longitudinal study of pre-adolescents aged 9-10 years, comprising 11,795 participants tracked over 36 months. Structured interviews measuring SLEs were conducted using the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS). Initial linear regression analyses explored if SLEs could predict volumes of the right and left NAc. Subsequently, Cox regression models were used to investigate how SLEs and NAc volume might predict the initiation of tobacco and marijuana use, with the analysis stratified by sex to address potential sex differences.

Results

Our findings reveal that SLEs significantly predicted marijuana use in males but not in females, and tobacco use was influenced by SLEs in both sexes. A higher number of SLEs was linked with decreased left NAc volume in males, a trend not seen in females. The right NAc volume did not predict substance use in either sex. However, volumes of both the right and left NAc were significant predictors of future tobacco use, with varying relationships across sexes. In females, an inverse relationship was observed between both NAc volumes and the risk of tobacco use. In contrast, a positive correlation existed between the left NAc volume and tobacco and marijuana use in males, with no such relationship for females.

Conclusion

This study underscores that the associations between SLEs, NAc volume, and subsequent substance use are influenced by a nuanced interplay of sex, brain hemisphere, and substance type.

Automated Mapping of Residual Distortion Severity in Diffusion MRI

Susceptibility-induced distortion is a common artifact in diffusion MRI (dMRI), which deforms the dMRI locally and poses significant challenges in connectivity analysis. While various methods were proposed to correct the distortion, residual distortions often persist at varying degrees across brain regions and subjects. Generating a voxel-level residual distortion severity map can thus be a valuable tool to better inform downstream connectivity analysis. To fill this current gap in dMRI analysis, we propose a supervised deep-learning network to predict a severity map of residual distortion. The training process is supervised using the structural similarity index measure (SSIM) of the fiber orientation distribution (FOD) in two opposite phase encoding (PE) directions. Only b0 images and related outputs from the distortion correction methods are needed as inputs in the testing process. The proposed method is applicable in large-scale datasets such as the UK Biobank, Adolescent Brain Cognitive Development (ABCD), and other emerging studies that only have complete dMRI data in one PE direction but acquires b0 images in both PEs. In our experiments, we trained the proposed model using the Lifespan Human Connectome Project Aging (HCP-Aging) dataset ( n = 662 ) and apply the trained model to data ( n = 1330 ) from UK Biobank. Our results show low training, validation, and test errors, and the severity map correlates excellently with an FOD integrity measure in both HCP-Aging and UK Biobank data. The proposed method is also highly efficient and can generate the severity map in around 1 second for each subject.

Unraveling how the adolescent brain deals with criticism using dynamic causal modeling

Sensitivity to criticism, which can be defined as a negative evaluation that a person receives from someone else, is considered a risk factor for the development of psychiatric disorders in adolescents. They may be more vulnerable to social evaluation than adults and exhibit more inadequate emotion regulation strategies such as rumination. The neural network involved in dealing with criticism in adolescents may serve as a biomarker for vulnerability to depression. However, the directions of the functional interactions between the brain regions within this neural network in adolescents are still unclear. In this study, 64 healthy adolescents (aged 14 to 17 years) were asked to listen to a series of self-referential auditory segments, which included negative (critical), positive (praising), and neutral conditions, during fMRI scanning. Dynamic Causal Modeling (DCM) with Parametric Empirical Bayesian (PEB) analysis was performed to map the interactions within the neural network that was engaged during the processing of these segments. Three regions were identified to form the interaction network: the left pregenual anterior cingulate cortex (pgACC), the left dorsolateral prefrontal cortex (DLPFC), and the right precuneus (preCUN). We quantified the modulatory effects of exposure to criticism and praise on the effective connectivity between these brain regions. Being criticized was found to significantly inhibit the effective connectivity from the preCUN to the DLPFC. Adolescents who scored high on the Perceived Criticism Measure (PCM) showed less inhibition of the preCUN-to-DLPFC connectivity when being criticized, which may indicate that they required more engagement of the Central Executive Network (which includes the DLPFC) to sufficiently disengage from negative self-referential processing. Furthermore, the inhibitory connectivity from the DLPFC to the pgACC was strengthened by exposure to praise as well as criticism, suggesting a recruitment of cognitive control over emotional responses when dealing with positive and negative evaluative feedback. Our novel findings contribute to a more profound understanding of how criticism affects the adolescent brain and can help to identify potential biomarkers for vulnerability to develop mood disorders before or during adulthood.

Cognitive stimulation as a mechanism linking socioeconomic status and neural function supporting working memory: a longitudinal fMRI study

Childhood experiences of low socioeconomic status are associated with alterations in neural function in the frontoparietal network and ventral visual stream, which may drive differences in working memory. However, the specific features of low socioeconomic status environments that contribute to these disparities remain poorly understood. Here, we examined experiences of cognitive deprivation (i.e. decreased variety and complexity of experience), as opposed to experiences of threat (i.e. violence exposure), as a potential mechanism through which family income contributes to alterations in neural activation during working memory. As part of a longitudinal study, 148 youth between aged 10 and 13 years completed a visuospatial working memory fMRI task. Early childhood low income, chronicity of low income in early childhood, and current income-to-needs were associated with task-related activation in the ventral visual stream and frontoparietal network. The association of family income with decreased activation in the lateral occipital cortex and intraparietal sulcus during working memory was mediated by experiences of cognitive deprivation. Surprisingly, however, family income and deprivation were not significantly related to working memory performance, and only deprivation was associated with academic achievement in this sample. Taken together, these findings suggest that early life low income and associated cognitive deprivation are important factors in neural function supporting working memory.

Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy

Introduction

The externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are common in adolescence and are strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap complicates intervention planning. Understanding which factors predict the onset of each disorder and disambiguating their different predictors is of substantial translational interest.

Materials and methods

We analyzed 5,777 multimodal candidate predictors from children aged 9-10 years and their parents in the ABCD cohort to predict the future onset of ADHD, ODD, and CD at 2-year follow-up. We used deep learning optimized with an innovative AI algorithm to jointly optimize model training, perform automated feature selection, and construct individual-level predictions of illness onset and all prevailing cases at 11-12 years and examined relative predictive performance when candidate predictors were restricted to only neural metrics.

Results

Multimodal models achieved ~86-97% accuracy, 0.919-0.996 AUROC, and ~82-97% precision and recall in testing in held-out, unseen data. In neural-only models, predictive performance dropped substantially but nonetheless achieved accuracy and AUROC of ~80%. Parent aggressive and externalizing traits uniquely differentiated the onset of ODD, while structural MRI metrics in the limbic system were specific to CD. Psychosocial measures of sleep disorders, parent mental health and behavioral traits, and school performance proved valuable across all disorders. In neural-only models, structural and functional MRI metrics in subcortical regions and cortical-subcortical connectivity were emphasized. Overall, we identified a strong correlation between accuracy and final predictor importance.

Conclusion

Deep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders with the same design and participant cohort to enable direct comparisons, analyze >200 multimodal features, and include many types of neuroimaging metrics. Future study to test our observations in external validation data will help further test the generalizability of these findings.

Caffeinated Soda Intake in Children Is Associated with Neurobehavioral Risk Factors for Substance Misuse

BACKGROUND AND OBJECTIVES

Use of psychotropic substances in childhood has been associated with both impulsivity and other manifestations of poor executive function as well as escalation over time to use of progressively stronger substances. However, how this relationship may start in earlier childhood has not been well explored. Here, we investigated the neurobehavioral correlates of daily caffeinated soda consumption in preadolescent children and examined whether caffeinated soda intake is associated with a higher risk of subsequent alcohol initiation.

METHODS

Using Adolescent Brain Cognitive Development study data (N = 2,092), we first investigated cross-sectional relationships between frequent caffeinated soda intake and well-known risk factors of substance misuse: impaired working memory, high impulsivity, and aberrant reward processing. We then examined whether caffeinated soda intake at baseline predicts more alcohol sipping at 12 months follow-up using a machine learning algorithm.

RESULTS

Daily consumption of caffeinated soda was cross-sectionally associated with neurobehavioral risk factors for substance misuse such as higher impulsivity scores and lower working memory performance. Furthermore, caffeinated soda intake predicted a 2.04 times greater likelihood of alcohol sipping after 12 months, even after controlling for rates of baseline alcohol sipping rates.

CONCLUSIONS

These findings suggest that previous linkages between caffeine and substance use in adolescence also extend to younger initiation, and may stem from core neurocognitive features thought conducive to substance initiation.

Living on the edge: network neuroscience beyond nodes

Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective - namely one that emphasizes 'edges' - may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed 'edge-centric' method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.

Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence

Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample and multidomain high-dimension data, we used 160 candidate predictors encompassing neural, prenatal, developmental, physiologic, sociocultural, environmental, emotional and cognitive features and leveraged three different machine learning algorithms optimized with a novel artificial intelligence meta-learning technique to predict individual cases of anxiety, depression, attention deficit, disruptive behaviors and post-traumatic stress. Our models tested well in unseen, held-out data (AUC ≥ 0.94). By utilizing a large-scale design and advanced computational approaches, we were able to compare the relative predictive ability of neural versus psychosocial features in a principled manner and found that psychosocial features consistently outperformed neural metrics in their relative ability to deliver robust predictions of individual cases. We found that deep learning with artificial neural networks and tree-based learning with XGBoost outperformed logistic regression with ElasticNet, supporting the conceptualization of mental illnesses as multifactorial disease processes with non-linear relationships among predictors that can be robustly modeled with computational psychiatry techniques. To our knowledge, this is the first study to test the relative predictive ability of these gold-standard algorithms from different classes across multiple mental health conditions in youth within the same study design in multidomain data utilizing >100 candidate predictors. Further research is suggested to explore these findings in longitudinal data and validate results in an external dataset.

Brief mock-scan training reduces head motion during real scanning for children: A growth curve study

Pediatric neuroimaging datasets are rapidly increasing in scales. Despite strict protocols in data collection and preprocessing focused on improving data quality, the presence of head motion still impedes our understanding of neurodevelopmental mechanisms. Large head motion can lead to severe noise and artifacts in magnetic resonance imaging (MRI) studies, inflating correlations between adjacent brain areas and decreasing correlations between spatial distant territories, especially in children and adolescents. Here, by leveraging mock-scans of 123 Chinese children and adolescents, we demonstrated the presence of increased head motion in younger participants. Critically, a 5.5-minute training session in an MRI mock scanner was found to effectively suppress the head motion in the children and adolescents. Therefore, we suggest that mock scanner training should be part of the quality assurance routine prior to formal MRI data collection, particularly in large-scale population-level neuroimaging initiatives for pediatrics.

Nicotine dependence and insula subregions: functional connectivity and cue-induced activation

Nicotine dependence is a major predictor of relapse in people with Tobacco Use Disorder (TUD). Accordingly, therapies that reduce nicotine dependence may promote sustained abstinence from smoking. The insular cortex has been identified as a promising target in brain-based therapies for TUD, and has three major sub-regions (ventral anterior, dorsal anterior, and posterior) that serve distinct functional networks. How these subregions and associated networks contribute to nicotine dependence is not well understood, and therefore was the focus of this study. Sixty individuals (28 women; 18-45 years old), who smoked cigarettes daily, rated their level of nicotine dependence (on the Fagerström Test for Nicotine Dependence) and, after abstaining from smoking overnight (~12 h), underwent functional magnetic resonance imaging (fMRI) in a resting state. A subset of these participants (N = 48) also completing a cue-induced craving task during fMRI. Correlations between nicotine dependence and resting-state functional connectivity (RSFC) and cue-induced activation of the major insular sub-regions were evaluated. Nicotine dependence was negatively correlated with connectivity of the left and right dorsal, and left ventral anterior insula with regions within the superior parietal lobule (SPL), including the left precuneus. No relationship between posterior insula connectivity and nicotine dependence was found. Cue-induced activation in the left dorsal anterior insula was positively associated with nicotine dependence and negatively associated with RSFC of the same region with SPL, suggesting that craving-related responsivity in this subregion was greater among participants who were more dependent. These results may inform therapeutic approaches, such as brain stimulation, which may elicit differential clinical outcomes (e.g., dependence, craving) depending on the insular subnetwork that is targeted.

Gut-Brain Axis Perspective on Negative Symptoms and Their Neighbors in Early Adolescence: Can We Move Care Upstream?

The current study investigated symptom network patterns in adolescents from a gut-brain-axis (GBA) biopsychosocial perspective. Our secondary analysis of data from the Adolescent Brain Cognitive Development Study assessed symptom relationships using network analysis to provide information about multivariate structural dependencies among 41 signs and symptoms. Cross-sectional EBICglasso symptom networks were evaluated to assess patterns associated with anhedonia and depressed mood. Significant differences were identified between symptom neighbors of anhedonia compared with depressed mood based on stratification by age. The GBA perspective revealed several symptom neighbors that could expand clinical assessment, diagnosing criteria, education, and interventions for adolescents at risk for, or with, anhedonia or depressed mood. Results speak to the unique impact of symptoms on health that are not interchangeable with other symptoms and do not have equal effects. Mental health nurses should consider a holistic and proactive precision health approach to improving health and well-being through evidence-based assessment of symptom associations. [Journal of Psychosocial Nursing and Mental Health Services, xx(xx), xx-xx.].

Task-based co-activation patterns reliably predict resting state canonical network engagement during development

Neurodevelopmental research has traditionally focused on development of individual structures, yet multiple lines of evidence indicate parallel development of large-scale systems, including canonical neural networks (i.e., default mode, frontoparietal). However, the relationship between region- vs. network-level development remains poorly understood. The current study tests the ability of a recently developed multi-task coactivation matrix approach to predict canonical resting state network engagement at baseline and at two-year follow-up in a large and cohort of young adolescents. Pre-processed tabulated neuroimaging data were obtained from the Adolescent Brain and Cognitive Development (ABCD) study, assessing youth at baseline (N = 6073, age = 10.0 ± 0.6 years, 3056 female) and at two-year follow-up (N = 3539, age = 11.9 ± 0.6 years, 1726 female). Individual multi-task co-activation matrices were constructed from the beta weights of task contrasts from the stop signal task, the monetary incentive delay task, and emotional N-back task. Activation-based predictive modeling, a cross-validated machine learning approach, was adopted to predict resting-state canonical network engagement from multi-task co-activation matrices at baseline. Note that the tabulated data used different parcellations of the task fMRI data ("ASEG" and Desikan) and the resting-state fMRI data (Gordon). Despite this, the model successfully predicted connectivity within the default mode network (DMN, rho = 0.179 ± 0.002, p < 0.001) across participants and identified a subset of co-activations within parietal and occipital macroscale brain regions as key contributors to model performance, suggesting an underlying common brain functional architecture across cognitive domains. Notably, predictive features for resting-state connectivity within the DMN identified at baseline also predicted DMN connectivity at two-year follow-up (rho = 0.258). These results indicate that multi-task co-activation matrices are functionally meaningful and can be used to predict resting-state connectivity. Interestingly, given that predictive features within the co-activation matrices identified at baseline can be extended to predictions at a future time point, our results suggest that task-based neural features and models are valid predictors of resting state network level connectivity across the course of development. Future work is encouraged to verify these findings with more consistent parcellations between task-based and resting-state fMRI, and with longer developmental trajectories.

Editorial: The Epidemiology and Cognitive Characteristics of Attention-Deficit/Hyperactivity Disorder Depend on How Strictly the Disorder Is Defined

In this month's issue of the Journal, Cordova et al.¹ advance knowledge in our field by leveraging the Adolescent Brain Cognitive Development (ABCD) Study dataset to characterize the prevalence and comorbidities of attention-deficit/hyperactivity disorder (ADHD) in a large community sample. The ABCD dataset² is useful because it is one of the largest samples of its kind (N = 11,878) and includes data from multiple measures and from multiple informants. This allows for sophisticated latent variable approaches to define ADHD from both a categorical and a dimensional perspective. Latent variables are variables that are not directly observed but are detected via measurement of other directly observed variables and statistical modeling.³.

Elucidating the Neurobiologic Etiology of Comorbid PTSD and Substance Use Disorders

Early childhood maltreatment and other traumatic event experiences ("trauma") are common among youth, including those with substance use problems including substance use disorders (SUD). Particularly, interpersonal violence is associated with high rates of comorbidity between posttraumatic stress disorder (PTSD) and SUD, and these comorbid disorders exhibit high levels of overlapping symptomatology. Theoretical models proposed to explain the bidirectional relationship between PTSD and SUD include the self-medication hypothesis and susceptibility hypothesis. In this article, we explore neurobiologic changes associated with trauma, PTSD, and SUD that underly dysregulated stress response. Examining lessons learned from recent translational and clinical research, we propose that further elucidating the neurobiologic etiology of comorbid PTSD and SUD will require a collaborative, interdisciplinary approach, including the integration of preclinical and clinical studies, exploration of biologic markers in clinical studies, and accumulation of larger studies and longitudinal studies with the power to study PTSD and SUD. Such research can transform the field and ultimately reduce high rates and costly impairment of co-occurring PTSD and SUD across the lifespan.

Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine

Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication bias, and inaccessibility to the data underlying a publication ('dark data'). Co-analysis of pooled data at the level of individual research participants can offer higher levels of evidence, but this requires that researchers share raw individual participant data (IPD). FAIR (findable, accessible, interoperable, and reusable) data governance principles aim to guide data lifecycle management by providing a framework for actionable data sharing. Here we discuss the implications of FAIR for data harmonization, an essential step for pooling data for IPD analysis. We describe the harmonization-information trade-off, which states that the level of granularity in harmonizing data determines the amount of information lost. Finally, we discuss a framework for managing the trade-off and the levels of harmonization. In the coming era of funder mandates for data sharing, research communities that effectively manage data harmonization will be empowered to harness big data and advanced analytics such as machine learning and artificial intelligence tools, leading to stunning new discoveries that augment our understanding of diseases and their treatments. By elevating scientific data to the status of a first-class citizen of the scientific enterprise, there is strong potential for biomedicine to transition from a narrative publication product orientation to a modern data-driven enterprise where data itself is viewed as a primary work product of biomedical research.

Geometric effects of volume-to-surface mapping of fMRI data

In this work, we identify a problem with the process of volume-to-surface mapping of functional Magnetic Resonance Imaging (fMRI) data that emerges in local connectivity analysis. We show that neighborhood correlations on the surface of the brain vary spatially with the gyral structure, even when the underlying volumetric data are uncorrelated noise. This could potentially have impacted studies focusing upon local neighborhood connectivity. We explore the effects of this anomaly across varying data resolutions and surface mesh densities, and propose several measures to mitigate these unwanted effects.

Brain atrophy in middle age using magnetic resonance imaging scans from Japan’s health screening programme

Although health screening plays a key role in the management of chronic diseases associated with lifestyle choices, brain health is not generally monitored, remaining a black box prior to the manifestation of clinical symptoms. Japan is unique in this regard, as brain MRI scans have been widely performed for more than two decades as part of Brain Dock, a comprehensive health screening programme. A vast number of stored images (well over a million) of longitudinal scans and extensive health data are available, offering a valuable resource for investigating the prevalence of various types of brain-related health conditions occurring throughout adulthood.

In this paper, we report on the findings of our preliminary quantitative analysis of T₁-weighted MRIs of the brain obtained from 13 980 subjects from three participating sites during the period 2015–19. We applied automated segmentation analysis and observed age-dependent volume loss of various brain structures. We subsequently investigated the effects of scan protocols and the feasibility of calibration for pooling the data. Last, the degree of brain atrophy was correlated with four known risk factors of dementia; blood glucose level, hypertension, obesity, and alcohol consumption.

In this initial analysis, we identified brain ventricular volume as an effective marker of age-dependent brain atrophy, being highly sensitive to ageing and evidencing strong robustness against protocol variability. We established the normal range of ventricular volumes at each age, which is an essential first step for establishing criteria used to interpret data obtained for individual participants. We identified a subgroup of individuals at midlife with ventricles that substantially exceeded the average size. The correlation studies revealed that all four risk factors were associated with greater ventricular volumes at midlife, some of which reached highly significant sizes.

This study demonstrates the feasibility of conducting a large-scale quantitative analysis of existing Brain Dock data in Japan. It will importantly guide future efforts to investigate the prevalence of large ventricles at midlife and the potential reduction of this prevalence, and hence of dementia risk, through lifestyle changes.

Fast construction of interpretable whole-brain decoders

Researchers often seek to decode mental states from brain activity measured with functional MRI. Rigorous decoding requires the use of formal neural prediction models, which are likely to be the most accurate if they use the whole brain. However, the computational burden and lack of interpretability of off-the-shelf statistical methods can make whole-brain decoding challenging. Here, we propose a method to build whole-brain neural decoders that are both interpretable and computationally efficient. We extend the partial least squares algorithm to build a regularized model with variable selection that offers a unique "fit once, tune later" approach: users need to fit the model only once and can choose the best tuning parameters post hoc. We show in real data that our method scales well with increasing data size and yields interpretable predictors. The algorithm is publicly available in multiple languages in the hope that interpretable whole-brain predictors can be implemented more widely in neuroimaging research.

A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

As the largest longitudinal study of adolescent brain development and behavior to date, the Adolescent Brain Cognitive Development (ABCD) Study® has provided immense opportunities for researchers across disciplines since its first data release in 2018. The size and scope of the study also present a number of hurdles, which range from becoming familiar with the study design and data structure to employing rigorous and reproducible analyses. The current paper is intended as a guide for researchers and reviewers working with ABCD data, highlighting the features of the data (and the strengths and limitations therein) as well as relevant analytical and methodological considerations. Additionally, we explore justice, equity, diversity, and inclusion efforts as they pertain to the ABCD Study and other large-scale datasets. In doing so, we hope to increase both accessibility of the ABCD Study and transparency within the field of developmental cognitive neuroscience.

Reliability and stability challenges in ABCD task fMRI data

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and long-term stability of individual differences in task-fMRI measures using fMRI measures of brain activation provided by the adolescent brain cognitive development (ABCD) Study Release v4.0 as an individual's average regional activity, using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability (quantified as the ratio of non-scanner related stable variance to all variances) was poor in virtually all brain regions, with an average value of 0.088 and 0.072 for short term (within-session) reliability and long-term (between-session) stability, respectively, in regions of interest (ROIs) historically-recruited by the tasks. Only one reliability or stability value in ROIs exceeded the 'poor' cut-off of 0.4, and in fact rarely exceeded 0.2 (only 4.9%). Motion had a pronounced effect on estimated reliability/stability, with the lowest motion quartile of participants having a mean reliability/stability 2.5 times higher (albeit still 'poor') than the highest motion quartile. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue urgently needs to be addressed through optimization of task design, scanning parameters, data acquisition protocols, preprocessing pipelines, and data denoising methods.

Sensitization-based risk for substance abuse in vulnerable individuals with ADHD: Review and re-examination of evidence

Evidence of sensitization following stimulants administration in humans is just emerging, which prevents reaching more definitive conclusions in favor or against a purported protective role of stimulant treatments for ADHD for the development of substance use disorders. Existing evidence from both animal and human research suggest that stimulants produce neurophysiological changes in the brain reward system, some of which could be persistent. This could be relevant in choosing optimal treatments for young patients with ADHD who have additional clinical risk factors for substance abuse (e.g. conduct disorder (CD) and/or familial addictions). Here we stipulate that, while the majority of youth with ADHD greatly benefit from treatments with stimulants, there might be a subpopulation of individuals whose neurobiological profiles may confer risk for heightened vulnerability to the effects of stimulants on the responsiveness of the brain reward system. We propose that focused human research is needed to elucidate the unknown effects of prolonged stimulant exposure on the neurophysiology of the brain reward system in young patients with ADHD.

The Emotional Word-Emotional Face Stroop task in the ABCD study: Psychometric validation and associations with measures of cognition and psychopathology

Characterizing the interactions among attention, cognitive control, and emotion during adolescence may provide important insights into why this critical developmental period coincides with a dramatic increase in risk for psychopathology. However, it has proven challenging to develop a single neurobehavioral task that simultaneously engages and differentially measures these diverse domains. In the current study, we describe properties of performance on the Emotional Word-Emotional Face Stroop (EWEFS) task in the Adolescent Brain Cognitive Development (ABCD) Study, a task that allows researchers to concurrently measure processing speed/attentional vigilance (i.e., performance on congruent trials), inhibitory control (i.e., Stroop interference effect), and emotional information processing (i.e., difference in performance on trials with happy as compared to angry distracting faces). We first demonstrate that the task manipulations worked as designed and that Stroop performance is associated with multiple cognitive constructs derived from different measures at a prior time point. We then show that Stroop metrics tapping these three domains are preferentially associated with aspects of externalizing psychopathology and inattention. These results highlight the potential of the EWEFS task to help elucidate the longitudinal dynamics of attention, inhibitory control, and emotion across adolescent development, dynamics which may be altered by level of psychopathology.

Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification

Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so as to improve classification performance and help us understand the pathological mechanism of AD-related brain disorders. Previous studies usually extract node statistics or edge weights from FBNs to represent each subject. However, these methods generally ignore the topological structure (such as modularity) of FBNs. To address this issue, we propose a modular-LASSO feature selection (MLFS) framework that can explicitly model the modularity information to identify discriminative and interpretable features from FBNs for automated AD/MCI classification. Specifically, the proposed MLFS method first searches the modular structure of FBNs through a signed spectral clustering algorithm, and then selects discriminative features via a modularity-induced group LASSO method, followed by a support vector machine (SVM) for classification. To evaluate the effectiveness of the proposed method, extensive experiments are performed on 563 resting-state functional MRI scans from the public ADNI database to identify subjects with AD/MCI from normal controls and predict the future progress of MCI subjects. Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD.

Sex-specific associations between subcortical morphometry in childhood and adult alcohol consumption: A 17-year follow-up study

Men and women tend to differ in the age of first alcohol consumption, transition into disordered drinking, and the prevalence of alcohol use disorder. Here, we use a unique longitudinal dataset to test for potentially predispositonal sex-biases in brain organization prior to initial alcohol exposure. Our study combines measures of subcortical morphometry gathered in alcohol naive individuals during childhood (mean age: 9.43 years, SD = 2.06) with self-report measures of alcohol use in the same individuals an average of 17 years later (N = 81, 46 males, 35 females). We observe that pediatric amygdala and hippocampus volume both show sex-biased relationships with adult drinking. Specifically, females show a stronger association between subcortical volumetric reductions in childhood and peak drinking in adulthood as compared to males. Detailed analysis of subcortical shape localizes these effects to the rostro-medial hippocampus and basolateral amygdala subnuclei. In contrast, we did not observe sex-specific associations between striatal anatomy and peak alcohol consumption. These results are consistent with a model in which organization of the amygdala and hippocampus in childhood is more relevant for subsequent patterns of peak alcohol use in females as compared to males. Differential neuroanatomical precursors of alcohol use in males and females could provide a potential developmental basis for well recognized sex-differences in alcohol use behaviors.. Thus, our findings not only indicate that brain correlates of human alcohol consumption are manifest long before alcohol initiation, but that some of these correlates are not equivalent between males and females.

Factors associated with severity, incidence or persistence of internet gaming disorder in children and adolescents: a 2-year longitudinal study

AIM

This study examined factors associated with severity, incidence and/or persistence of internet gaming disorder (IGD) in children and adolescents.

DESIGN, SETTING, PARTICIPANTS

Prospective cohort study with 2-year follow-up study in South Korea. A total of 2319 3rd-, 4th- and 7th-graders enrolled into the internet user cohort for unbiased recognition of gaming disorder in early adolescence [intramural Continuing Umbrella of Research Experiences (iCURE)] and analyzed for the current study.

MEASUREMENTS

Severity of the IGD features was assessed by the Internet Game Use-Elicited Symptom Screen (IGUESS), a self-reported questionnaire based on the DSM-5 IGD criteria. Participants with IGUESS scores ≥ 10 were considered to be at high risk for IGD (HIGD). Time spent playing on-line games, game types, depressive symptoms, trait anxiety, attention deficit hyperactivity disorder (ADHD) symptoms, social support, openness of communication with parents, attachment to parents and potential confounders were assessed by self-report.

FINDINGS

A total of 175 (7.5%) students were classified as HIGD cases upon initial assessment. The independent risk factors of incidence of HIGD included playing on-line games for ≥ 240 minutes/day, playing multi-player games, depressive symptoms and ADHD symptoms [adjusted rate ratios (aRRs) = 2.03, 1.63, 2.04, 2.53, respectively; all P < 0.05]. Factors related to changing IGD severity scores were playing on-line games for 60-239 minutes/day, playing single-player on-line games, higher attachment and social support [adjusted incidence rate ratio (aIRRs) = 1.38, 1.22, 0.86, 0.87, respectively; all P < 0.05]. Independent predictors of persistence of HIGD were playing on-line games for ≥ 240 minutes/day and ADHD symptoms (aRRs = 2.63, 2.14, respectively; all P < 0.05).

CONCLUSIONS

Among adolescents in South Korea, the existence of ADHD symptoms and spending more than 4 hours per day playing on-line games were associated with the occurrence or persistence of high risk for internet gaming disorder.

Altered effective connectivity of the reward network during an incentive-processing task in adults with alcohol use disorder

BACKGROUND

Abnormalities of reward sensitivity and impulsivity are known to be correlated with each other and alcohol use disorder (AUD) risk, but the underlying aberrant neural circuitry involved is not clearly defined. We sought to extend the current knowledge of AUD pathophysiology by studying incentive processing in persons with AUD using functional neuroimaging data.

METHODS

We utilized functional MRI data from the Human Connectome Project Database obtained during performance of a number-guessing incentive-processing task with win, loss, and neutral feedback conditions in 78 participants with either DSM-IV alcohol abuse or dependence (combined as the AUD group) and 78 age- and sex-matched control (CON) participants. Within a network consisting of anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), insula, ventral striatum, and dorsal striatum (DS) in the right hemisphere, we performed dynamic causal modeling analysis to test group-level differences (AUD vs. CON) in effective directional connectivity (EC) as modulated by "win" and "loss" conditions. We used linear regression analyses to characterize the relations between each EC outcome and measures of cumulative alcohol exposure and impulsivity.

RESULTS

During wins, AUD participants had lower ECs from ACC to the other four nodes, greater ECs from insula to the other four nodes, greater ECs from DLPFC to the other four nodes, and greater DS to DS self-connection EC than CON participants. In the total sample, EC from the insula to the DLPFC (insula → DLPFC) during wins was positively correlated with both impulsivity (as measured by the delay-discounting task) and cumulative alcohol exposure. The DS to DS self-connection EC during wins was positively correlated with impulsivity. Many of the altered ECs from the ACC and insula to other nodes were correlated with cumulative alcohol exposure.

CONCLUSIONS

Individuals with AUD have disrupted EC in both instrumentally driven and automatized corticostriatal reward circuits during non-alcohol reward feedback. These results point to disrupted corticostriatal EC in both "top-down" and "bottom-up" pathways among individuals with AUD.

Parental Educational Attainment, the Superior Temporal Cortical Surface Area, and Reading Ability among American Children: A Test of Marginalization-Related Diminished Returns

BACKGROUND

Recent studies have shown that parental educational attainment is associated with a larger superior temporal cortical surface area associated with higher reading ability in children. Simultaneously, the marginalization-related diminished returns (MDRs) framework suggests that, due to structural racism and social stratification, returns of parental education are smaller for black and other racial/ethnic minority children compared to their white counterparts.

PURPOSE

This study used a large national sample of 9-10-year-old American children to investigate associations between parental educational attainment, the right and left superior temporal cortical surface area, and reading ability across diverse racial/ethnic groups.

METHODS

This was a cross-sectional analysis that included 10,817 9-10-year-old children from the Adolescent Brain Cognitive Development (ABCD) study. Parental educational attainment was treated as a five-level categorical variable. Children's right and left superior temporal cortical surface area and reading ability were continuous variables. Race/ethnicity was the moderator. To adjust for the nested nature of the ABCD data, mixed-effects regression models were used to test the associations between parental education, superior temporal cortical surface area, and reading ability overall and by race/ethnicity.

RESULTS

Overall, high parental educational attainment was associated with greater superior temporal cortical surface area and reading ability in children. In the pooled sample, we found statistically significant interactions between race/ethnicity and parental educational attainment on children's right and left superior temporal cortical surface area, suggesting that high parental educational attainment has a smaller boosting effect on children's superior temporal cortical surface area for black than white children. We also found a significant interaction between race and the left superior temporal surface area on reading ability, indicating weaker associations for Alaskan Natives, Native Hawaiians, and Pacific Islanders (AIAN/NHPI) than white children. We also found interactions between race and parental educational attainment on reading ability, indicating more potent effects for black children than white children.

CONCLUSION

While parental educational attainment may improve children's superior temporal cortical surface area, promoting reading ability, this effect may be unequal across racial/ethnic groups. To minimize the racial/ethnic gap in children's brain development and school achievement, we need to address societal barriers that diminish parental educational attainment's marginal returns for middle-class minority families. Social and public policies need to go beyond equal access and address structural and societal barriers that hinder middle-class families of color and their children. Future research should test how racism, social stratification, segregation, and discrimination, which shape the daily lives of non-white individuals, take a toll on children's brains and academic development.

Substance Use Initiation, Particularly Alcohol, in Drug-Naive Adolescents: Possible Predictors and Consequences From a Large Cohort Naturalistic Study

OBJECTIVE

It is unclear whether deviations in brain and behavioral development, which may underpin elevated substance use during adolescence, are predispositions for or consequences of substance use initiation. Here, we examine behavioral and neuroimaging indices at early and mid-adolescence in drug-naive youths to identify possible predisposing factors for substance use initiation and its possible consequences.

METHOD

Among 304 drug-naive adolescents at baseline (age 14 years) from the IMAGEN dataset, 83 stayed drug-naive, 133 used alcohol on 1 to 9 occasions, 42 on 10 to 19 occasions, 27 on 20 to 39 occasions, and 19 on >40 occasions at follow-up (age 16 years). Baseline measures included brain activation during the Monetary Incentive Delay task. Data at both baseline and follow-up included measures of trait impulsivity and delay discounting.

RESULTS

From baseline to follow-up, impulsivity decreased in the 0 and 1- to 9-occasions groups (p < .004), did not change in the 10- to 19-occasions and 20- to 29-occasions groups (p > .294), and uncharacteristically increased in the >40-occasions group (p = .046). Furthermore, blunted medial orbitofrontal cortex activation during reward outcome at baseline significantly predicted higher alcohol use frequency at follow-up, above and beyond behavioral and clinical variables (p = .008).

CONCLUSION

These results suggest that the transition from no use to frequent drinking in early to mid-adolescence may disrupt normative developmental changes in behavioral control. In addition, blunted activity of the medial orbitofrontal cortex during reward outcome may underscore a predisposition toward the development of more severe alcohol use in adolescents. This distinction is clinically important, as it informs early intervention efforts in preventing the onset of substance use disorder in adolescents.

Parental Education and Left Lateral Orbitofrontal Cortical Activity during N-Back Task: An fMRI Study of American Adolescents

Introduction. The Orbitofrontal Cortex (OFC) is a cortical structure that has implications in cognition, memory, reward anticipation, outcome evaluation, decision making, and learning. As such, OFC activity correlates with these cognitive brain abilities. Despite research suggesting race and socioeconomic status (SES) indicators such as parental education may be associated with OFC activity, limited knowledge exists on multiplicative effects of race and parental education on OFC activity and associated cognitive ability. Purpose. Using functional brain imaging data from the Adolescent Brain Cognitive Development (ABCD) study, we tested the multiplicative effects of race and parental education on left lateral OFC activity during an N-Back task. In our study, we used a sociological rather than biological theory that conceptualizes race and SES as proxies of access to the opportunity structure and exposure to social adversities rather than innate and non-modifiable brain differences. We explored racial variation in the effect of parental educational attainment, a primary indicator of SES, on left lateral OFC activity during an N-Back task between Black and White 9–10 years old adolescents. Methods. The ABCD study is a national, landmark, multi-center brain imaging investigation of American adolescents. The total sample was 4290 9–10 years old Black or White adolescents. The independent variables were SES indicators, namely family income, parental education, and neighborhood income. The primary outcome was the average beta weight for N-Back (2 back versus 0 back contrast) in ASEG ROI left OFC activity, measured by functional Magnetic Resonance Imaging (fMRI) during an N-Back task. Ethnicity, age, sex, subjective SES, and family structure were the study covariates. For data analysis, we used linear regression models. Results. In White but not Black adolescents, parental education was associated with higher left lateral OFC activity during the N-Back task. In the pooled sample, we found a significant interaction between race and parental education on the outcome, suggesting that high parental education is associated with a larger increase in left OFC activity of White than Black adolescents. Conclusions. For American adolescents, race and SES jointly influence left lateral OFC activity correlated with cognition, memory, decision making, and learning. Given the central role of left lateral OFC activity in learning and memory, our finding calls for additional research on contextual factors that reduce the gain of SES for Black adolescents. Cognitive inequalities are not merely due to the additive effects of race and SES but also its multiplicative effects.

Nicotine addiction: Translational insights from circuit neuroscience

Contemporary neuroscience aims to understand how neuronal activity produces internal processes and observable behavioral states. This aim crucially depends on systems-level, circuit-based analyses of the working brain, as behavioral states arise from information flow and connectivity within and between discrete and overlapping brain regions, forming circuits and networks. Functional magnetic resonance imaging (fMRI), offers a key to advance circuit neuroscience; fMRI measures inter and intra- regional circuits at behaviorally relevant spatial-temporal resolution. Herein, we argue that cross-sectional observations in human populations can be best understood via mechanistic and causal insights derived from brain circuitry obtained from preclinical fMRI models. Using nicotine addiction as an exemplar of a circuit-based substance use disorder, we review fMRI-based observations of a circuit that was first shown to be disrupted among human smokers and was recently replicated in rodent models of nicotine dependence. Next, we discuss circuits that predispose to nicotine dependence severity and their interaction with circuits that change as a result of chronic nicotine administration using a rodent model of dependence. Data from both clinical and preclinical fMRI experiments argue for the utility of fMRI studies in translation and reverse translation of a circuit-based understanding of brain disease states. We conclude by discussing the future of circuit neuroscience and functional neuroimaging as an essential bridge between animal models and human populations to the understanding of brain function in health and disease.

Resting-State Functional Connectivity between Putamen and Salience Network and Childhood Body Mass Index

INTRODUCTION

Although the putamen has a significant role in reward-seeking and motivated behaviors, including eating and food-seeking, minorities' diminished returns (MDRs) suggest that individual-level risk and protective factors have weaker effects for Non-Hispanic Black than Non-Hispanic White individuals. However, limited research is available on the relevance of MDRs in terms of the role of putamen functional connectivity on body mass index (BMI).

PURPOSE

Building on the MDRs framework and conceptualizing race and socioeconomic status (SES) indicators as social constructs, we explored racial and SES differences in the associations between putamen functional connectivity to the salience network and children's BMI.

METHODS

For this cross-sectional study, we used functional magnetic resonance imaging (fMRI) data of 6473 9-10-year-old Non-Hispanic Black and Non-Hispanic White children from the Adolescent Brain Cognitive Development (ABCD) study. The primary independent variable was putamen functional connectivity to the salience network, measured by fMRI. The primary outcome was the children's BMI. Age, sex, neighborhood income, and family structure were the covariates. Race, family structure, parental education, and household income were potential moderators. For data analysis, we used mixed-effect models in the overall sample and by race.

RESULTS

Higher right putamen functional connectivity to the salience network was associated with higher BMI in Non-Hispanic White children. The same association was missing for Non-Hispanic Black children. While there was no overall association in the pooled sample, a significant interaction was found, suggesting that the association between right putamen functional connectivity to the salience network and children's BMI was modified by race. Compared to Non-Hispanic White children, Non-Hispanic Black children showed a weaker association between right putamen functional connectivity to the salience network and BMI. While parental education and household income did not moderate our association of interest, marital status altered the associations between putamen functional connectivity to the salience network and children's BMI. These patterns were observed for right but not left putamen. Other/Mixed Race children also showed a pattern similar to Non-Hispanic Black children.

CONCLUSIONS

The association between right putamen functional connectivity to the salience network and children's BMI may depend on race and marital status but not parental education and household income. While right putamen functional connectivity to the salience network is associated with Non-Hispanic White children's BMI, Non-Hispanic Black children' BMI remains high regardless of their putamen functional connectivity to the salience network. This finding is in line with MDRs, which attributes diminished effects of individual-risk and protective factors for Non-Hispanic Black children to racism, stratification, and segregation.

Neuroimaging in attention-deficit/hyperactivity disorder

PURPOSE OF REVIEW

Neuroimaging research on attention-deficit/hyperactivity disorder (ADHD) continues growing in extent and complexity, although it has yet to become clinically meaningful. We review recent MRI research on ADHD, to identify robust findings, current trends and challenges.

RECENT FINDINGS

We identified 40 publications between January 2019 and September 2020 reporting or reviewing MRI research on ADHD. Four meta-analyses have presented conflicting results regarding across-study convergence of functional and resting-state functional (fMRI and R-fMRI) studies on ADHD. On the other hand, the Enhancing NeuroImaging Genetics Through Meta-Analysis international consortium has identified statistically robust albeit small differences in structural brain cortical and subcortical indices in children with ADHD versus typically developing controls. Other international consortia are harnessing open-science efforts and multimodal data (imaging, genetics, phenotypic) to shed light on the complex interplay of genetics, environment, and development in the pathophysiology of ADHD. We note growing research in 'prediction' science, which applies machine-learning analysis to identify biomarkers of disease based on big data.

SUMMARY

Neuroimaging in ADHD is still far from informing clinical practice. Current large-scale, multimodal, and open-science initiatives represent promising paths toward untangling the neurobiology of ADHD.

The ups and downs of relating nondrug reward activation to substance use risk in adolescents

Purpose of review

A wealth of epidemiological and cohort research, together with a healthy dose of anecdote, has characterized late-adolescence and emerging adulthood as a time of increased substance use and other risky behaviors. This review will address whether differences between adolescents or between adolescents and other age groups in dopaminergic mesolimbic recruitment by (non-drug) rewards inferred from functional magnetic resonance imaging (fMRI) could partially explain morbidity and mortality from risky-behavior-related causes in adolescents.

Recent findings

Recent findings do not suggest a definitive directionality with regard to whether increased vs decreased mesolimbic responsiveness to nondrug rewards correlates with real-world risk-taking. Inconsistent relationships between reward-activation and real-world risky behavior in these reports reflect in part methodological differences as well as conceptual differences between populations in terms of whether tepid mesolimbic recruitment by rewards is a marker of psychiatric health.

Summary

There are several potential reasons why the directionality of relationships between reward-elicited brain activation and substance use risk (specifically) might differ. These factors include differences between adolescents in histories/exposure of substance use, motivation for substance use, the component of the instrumental behavior being studied, and the cognitive demands of the incentive tasks. Systematic manipulation of these discrepant study factors might offer a way forward to clarify how motivational neurocircuit function relates to addiction risk in adolescents.

Systematic Review: Medication Effects on Brain Intrinsic Functional Connectivity in Patients With Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

Resting-state functional magnetic resonance imaging (R-fMRI) studies of the neural correlates of medication treatment in attention-deficit/hyperactivity disorder (ADHD) have not been systematically reviewed. Our objective was to systematically identify, assess and summarize within-subject R-fMRI studies of pharmacological-induced changes in patients with ADHD. We critically appraised strengths and limitations, and provide recommendations for future research.

METHOD

Systematic review of published original reports in English meeting criteria in pediatric and adult patients with ADHD up to July 1, 2020. A thorough search preceded selection of studies matching prespecified criteria. Strengths and limitations of selected studies, regarding design and reporting, were identified based on current best practices.

RESULTS

We identified and reviewed 9 studies (5 pediatric and 4 adult studies). Sample sizes were small-medium (16-38 patients), and included few female participants. Medications were methylphenidate, amphetamines, and atomoxetine. Wide heterogeneity was observed in designs, analyses and results, which could not be combined quantitatively. Qualitatively, the multiplicity of brain regions and networks identified, some of which correlated with clinical improvements, do not support a coherent mechanistic hypothesis of medication effects. Overall, reports did not meet current standards to ensure reproducibility.

CONCLUSION

In this emerging field, the few studies using R-fMRI to analyze the neural correlates of medications in patients with ADHD suggest a potential modulatory effect of stimulants and atomoxetine on several intrinsic brain activity metrics. However, methodological heterogeneity and reporting issues need to be addressed in future research to validate findings which may contribute to clinical care. Such a goal is not yet at hand.

Resting-State fMRI to Identify the Brain Correlates of Treatment Response to Medications in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder: Lessons From the CUNMET Study

Neuroimaging research seeks to identify biomarkers to improve the diagnosis, prognosis, and treatment of attention-deficit/hyperactivity disorder (ADHD), although clinical translation of findings remains distant. Resting-state functional magnetic resonance imaging (R-fMRI) is increasingly being used to characterize functional connectivity in the brain. Despite mixed results to date and multiple methodological challenges, dominant hypotheses implicate hyperconnectivity across brain networks in patients with ADHD, which could be the target of pharmacological treatments. We describe the experience and results of the Clínica Universidad de Navarra (Spain) Metilfenidato (CUNMET) pilot study. CUNMET tested the feasibility of identifying R-fMRI markers of clinical response in children with ADHD undergoing naturalistical pharmacological treatments. We analyzed cross-sectional data from 56 patients with ADHD (18 treated with methylphenidate, 18 treated with lisdexamfetamine, and 20 treatment-naive patients). Standard preprocessing and statistical analyses with attention to control for head motion and correction for multiple comparisons were performed. The only results that survived correction were noted in contrasts of children who responded clinically to lisdexamfetamine after long-term treatment vs. treatment-naive patients. In these children, we observed stronger negative correlations (anticorrelations) across nodes in six brain networks, which is consistent with higher across-network functional segregation in patients treated with lisdexamfetamine, i.e., less inter-network interference than in treatment-naive patients. We also note the lessons learned, which could help those pursuing clinically relevant multidisciplinary research in ADHD en route to eventual personalized medicine. To advance reproducible open science, our report is accompanied with links providing access to our data and analytic scripts.

Systematic Review and Meta-analysis: Resting-State Functional Magnetic Resonance Imaging Studies of Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

To conduct a meta-analysis of resting-state functional magnetic resonance imaging (R-fMRI) studies in children and adolescents with attention-deficit/hyperactivity disorder (ADHD) and in adults with ADHD to assess spatial convergence of findings from available studies.

METHOD

Based on a preregistered protocol in PROSPERO (CRD42019119553), a large set of databases were searched up to April 9, 2019, with no language or article type restrictions. Study authors were systematically contacted for additional unpublished information/data. Resting-state functional magnetic resonance imaging studies using seed-based connectivity (SBC) or any other method (non-SBC) reporting whole-brain results of group comparisons between participants with ADHD and typically developing controls were eligible. Voxelwise meta-analysis via activation likelihood estimation with cluster-level familywise error (voxel-level: p < .001; cluster-level: p < .05) was used.

RESULTS

Thirty studies (18 SBC and 12 non-SBC), comprising 1,978 participants (1,094 with ADHD; 884 controls) were retained. The meta-analysis focused on SBC studies found no significant spatial convergence of ADHD-related hyperconnectivity or hypoconnectivity across studies. This nonsignificant finding remained after integrating 12 non-SBC studies into the main analysis and in sensitivity analyses limited to studies including only children or only non-medication-naïve patients.

CONCLUSION

The lack of significant spatial convergence may be accounted for by heterogeneity in study participants, experimental procedures, and analytic flexibility as well as in ADHD pathophysiology. Alongside other neuroimaging meta-analyses in other psychiatric conditions, the present results should inform the conduct and publication of future neuroimaging studies of psychiatric disorders.

Parental Education, Household Income, and Cortical Surface Area among 9-10 Years Old Children: Minorities' Diminished Returns

Introduction: Although the effects of parental education and household income on children's brain development are well established, less is known about possible variation in these effects across diverse racial and ethnic groups. According to the Minorities' Diminished Returns (MDRs) phenomenon, due to structural racism, social stratification, and residential segregation, parental educational attainment and household income show weaker effects for non-White than White children. Purpose: Built on the MDRs framework and conceptualizing race as a social rather than a biological factor, this study explored racial and ethnic variation in the magnitude of the effects of parental education and household income on children's whole-brain cortical surface area. Methods: For this cross-sectional study, we used baseline socioeconomic and structural magnetic resonance imaging (sMRI) data of the Adolescent Brain Cognitive Development (ABCD) study. Our analytical sample was 10,262 American children between ages 9 and 10. The independent variables were parental education and household income. The primary outcome was the children's whole-brain cortical surface area. Age, sex, and family marital status were covariates. Race and ethnicity were the moderators. We used mixed-effects regression models for data analysis as participants were nested within families and study sites. Results: High parental education and household income were associated with larger children's whole-brain cortical surface area. The effects of high parental education and high household income on children's whole-brain cortical surface area were modified by race. Compared to White children, Black children showed a diminished return of high parental education on the whole-brain cortical surface area when compared to White children. Asian American children showed weaker effects of household income on the whole-brain cortical surface area when compared to White children. We could not find differential associations between parental education and household income with the whole-brain cortical surface area, when compared to White children, for non-Hispanic and Hispanic children. Conclusions: The effects of parental educational attainment and household income on children's whole-brain cortical surface area are weaker in non-White than White families. Although parental education and income contribute to children's brain development, these effects are unequal across racial groups.

Behavioral and brain signatures of substance use vulnerability in childhood

The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [r_p]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (r_p: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which—together with mechanistic perspectives—may inform strategies aimed at early identification of risk for addiction.

Gray matter volumetric correlates of behavioral activation and inhibition system traits in children: An exploratory voxel-based morphometry study of the ABCD project data

Approach and avoidance represent two fundamental behavioral traits that develop early in life. Previous studies have examined the neural correlates of approach and avoidance traits in adults and adolescents. Here, using the data set of the Adolescent Brain Cognition Development project, we investigated the structural cerebral bases of behavioral activation system (BAS) and behavioral inhibition system (BIS) in children. We employed voxel-based morphometry to examine how gray matter volumes (GMV) related specifically to BAS and BIS traits in 11,542 children (5491 girls, age 9-10 years) with 648 and 2697 identified as monozygotic twins (MZ) and dizygotic twins/siblings (DZ), respectively. After accounting for the BIS score, higher BAS scores (residuals) were positively correlated with the GMV of the ventral striatum (VS), and the correlation was stronger in MZ than in DZ and unrelated children, with a heritability (h²) of 0.8463. Higher BAS scores were negatively correlated with the GMV of bilateral visual, lateral orbitofrontal, temporal, and inferior frontal cortex, as well as the precuneus. Higher BIS (after accounting for BAS) scores were negatively correlated with the GMVs of the ventral caudate and bilateral putamen/pallidum, hypothalamus, and right anterior insula, and the correlation was stronger in MZ than in DZ and unrelated children, with a heritability of 0.8848. A cluster in the VS showed positive and negative correlation with the BAS and BIS scores, respectively. These findings suggest shared and distinct cerebral volumetric bases of the BAS and BIS traits in children. Whereas both traits have a strong genetic basis, the BAS relative to BIS appears to be more amenable to environmental influences. These findings add to the literature of developmental neuroscience and may help identify genetic risk factors of externalizing and internalizing psychopathology.

Nucleus Accumbens Functional Connectivity with the Frontoparietal Network Predicts Subsequent Change in Body Mass Index for American Children

Background: Nucleus accumbens (NAc) is a brain structure with a well-established role in the brain reward processing system. Altered function of the NAc is shown to have a role in the development of food addiction and obesity. However, less is known about sex differences in the role of NAc function as a predictor of children’s change in body mass index (BMI) over time. Aim: We used the Adolescent Brain Cognitive Development data (version 2.01) to investigate sex differences in the predictive role of the NAc functional connectivity with the frontoparietal network on children’s BMI change over a one-year follow-up period. Methods: This 1-year longitudinal study successfully followed 3784 9–10-year-old children. Regression models were used to analyze the data. The predictor variable was NAc functional connectivity with the frontoparietal network measured using resting-state functional magnetic resonance imaging (fMRI). The primary outcome was BMI at the end of the 1-year follow up. Covariates included race, ethnicity, age, socioeconomic factors, and baseline BMI. Sex was the effect modifier. Results: NAc functional connectivity with the frontoparietal network was predictive of BMI changes over time. This association remained significant above and beyond all covariates. The above association, however, was only significant in female, not male children. Conclusion: The epidemiological observation that NAc functional connectivity is associated with BMI changes in children is an extension of well-controlled laboratory studies that have established the role of the NAc in the brain reward processing. More research is needed on sex differences in the brain regions that contribute to childhood obesity.

The YOUth cohort study: MRI protocol and test-retest reliability in adults

The YOUth cohort study is a unique longitudinal study on brain development in the general population. As part of the YOUth study, 2000 children will be included at 8, 9 or 10 years of age and planned to return every three years during adolescence. Magnetic resonance imaging (MRI) brain scans are collected, including structural T1-weighted imaging, diffusion-weighted imaging (DWI), resting-state functional MRI and task-based functional MRI. Here, we provide a comprehensive report of the MR acquisition in YOUth Child & Adolescent including the test-retest reliability of brain measures derived from each type of scan. To measure test-retest reliability, 17 adults were scanned twice with a week between sessions using the full YOUth MRI protocol. Intraclass correlation coefficients were calculated to quantify reliability. Global brain measures derived from structural T1-weighted and DWI scans were reliable. Resting-state functional connectivity was moderately reliable, as well as functional brain measures for both the inhibition task (stop versus go) and the emotion task (face versus house). Our results complement previous studies by presenting reliability results of regional brain measures collected with different MRI modalities. YOUth facilitates data sharing and aims for reliable and high-quality data. Here we show that using the state-of-the art YOUth MRI protocol brain measures can be estimated reliably.

Alcohol's Unique Effects on Cognition in Women: A 2020 (Re)view to Envision Future Research and Treatment

Alcohol use and misuse is increasing among women. Although the prevalence of drinking remains higher in men than women, the gender gap is narrowing. This narrative review focuses on the cognitive sequelae of alcohol consumption in women. Studies of acute alcohol effects on cognition indicate that women typically perform worse than men on tasks requiring divided attention, memory, and decision-making. Beneficial effects of moderate alcohol consumption on cognition have been reported; however, a number of studies have cautioned that other factors may be driving that association. Although chronic heavy drinking affects working memory, visuospatial abilities, balance, emotional processing, and social cognition in women and men, sex differences mark the severity and specific profile of functional deficits. The accelerated or compressed progression of alcohol-related problems and their consequences observed in women relative to men, referred to as "telescoping," highlights sex differences in the pharmacokinetics, pharmacodynamics, cognitive, and psychological consequences of alcohol. Brain volume deficits affecting multiple systems, including frontolimbic and frontocerebellar networks, contribute to impairment. Taken together, sex-related differences highlight the complexity of this chronic disease in women and underscore the relevance of examining the roles of age, drinking patterns, duration of abstinence, medical history, and psychiatric comorbidities in defining and understanding alcohol-related cognitive impairment.